Probabilistic Graphical Models 2 Inference

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

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Syllabus

Syllabus - What you will learn from this course
Week 1
Inference Overview
Variable Elimination
Week 2
Belief Propagation Algorithms
Week 3
MAP Algorithms
Week 4
Sampling Methods
Inference in Temporal Models
Week 5
Inference Summary

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Learning Outcomes: By the end of this course, you will be able to take a given PGM and

Execute the basic steps of a variable elimination or message passing algorithm

Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible

Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings

Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective

Design Metropolis Hastings proposal distributions that are more likely to give good results

Compute a MAP assignment by exact inference

Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

Reviews

Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.

Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts

Amazing course offering a technical as well as intuitional understanding of the principles of doing inference

Great introduction.

It would be great to have more examples included in the lectures and slides.